The aim of the EFAS post-processing methodology is to adjust the EFAS medium-range ensemble forecasts at specific locations, so they become predictors of future observed river discharge values. The EFAS post-processing methodology is based on a combination of two post-processing techniques: the Model Conditional Processor (MCP; Todini, 2008) and the Ensemble Model Output Statistics (EMOS; Gneiting et al., 2005) method. The post-processed forecast is represented by a probability distribution that is dependent on recent observations, simulation forced by observations (also known as the EFAS reanalysis), and forecasts. The output of this process is the 'Real-time Hydrograph' which is available in the pop-out windows of of the Reporting Point layer for static reporting points where near real-time and past river discharge observations are available. Since EFAS version 4.5, the post-processing has been performed at 6-hourly timesteps where possible.
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Figure 1: An example of the estimated river discharge distribution for a station from the off-line calibration. Orange shows the part estimated by the Generalised Pareto distribution. Purple shows the main part of the distribution. Small black lines show the individual river discharge values. Modified from Matthews et al. , (2022).
- Estimation of a joint probability distribution of observations and simulations across multiple timesteps. The joint probability distribution describes the relationship between observed and simulated values at different times over a 55-day period. Figure 2 shows an example of a joint distribution between 2 variables (a simulated variable (model) and an observed variable (reality)). The joint-distribution defined in the off-line calibration is between 440 variables for the 6-hourly stations and 110 variables for the daily stations.
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Figure 2: Representation of the joint probability distribution of observations (reality) and simulations (model), figure from Biondi , Daniela & Todini, Ezioet al. (2018). Comparing Hydrological Postprocessors Including Ensemble Predictions Into Full Predictive Probability Distribution of Streamflow. Water Resources Research. 10.1029/2017WR022432. https://doi.org/10.1029/2017WR022432
The distributions defined in the offline calibration are used in the forecast update part of the post-processing method. The length of the observation record and the quality of the observations can impact the accuracy of the distributions.
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If an insufficient number of near real-time river discharge observations are made available to EFAS, the Real-time hydrograph will show as:
References
Biondi, Daniela & Todini, Ezio. (2018). Comparing Hydrological Postprocessors Including Ensemble Predictions Into Full Predictive Probability Distribution of Streamflow. Water Resources Research. 10.1029/2017WR022432. https://doi.org/10.1029/2017WR022432
Gneiting, T., Raftery, A. E., Westveld, A. H., & Goldman, T. (2005). Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Monthly Weather Review, 133(5), 1098-1118.
Matthews, G., Barnard, C., Cloke, H., Dance, S. L., Jurlina, T., Mazzetti, C., & Prudhomme, C. (2022). Evaluating the impact of post-processing medium-range ensemble streamflow forecasts from the European Flood Awareness System. Hydrology and Earth System Sciences, 26(11), 2939-2968.
Todini, E. (2008). A model conditional processor to assess predictive uncertainty in flood forecasting. International Journal of River Basin Management, 6(2), 123-137.